Spatial distribution characteristics of soil heavy metals in Sabao Chaqu watershed of Tuotuo river, Qinghai-Tibet Plateau based on geographic detector

The Qinghai-Tibet Plateau belongs to the area of extremely fragile environment and sensitive to human activities. In recent years, more and more human interference has been detected in this area. In this study, 128 surface soil samples were collected from the Sabao Chaqu watershed of the Tuotuo river at the source of the Yangtze River on the Qinghai-Tibet Plateau. The soil pollution status and spatial distribution characteristics of Cd, Hg, As, Cu, Pb, Cr, Zn and Ni were evaluated by soil accumulation index, enrichment factor, pollution index and geographical detector. The results showed that the average contents of As, Cd, Pb and Zn in the study area were 1.2–3.64 times higher than soil background values of Tibet, while the contents of Hg, Cr, Cu and Ni were lower than the background values, while the average content of As was higher than the soil pollution risk screening value (GB15618-2018), and the pollution index showed that As was in a low pollution state, while the other 7 heavy metals were in a safe state. There were significant differences in the spatial distribution of 8 heavy metals and there was a significant correlation with soil properties and distance factors. Factor detection showed that natural factors had the strongest explanatory power to the contents of As, Cd, Cr, Cu and Ni, distance from the lake and soil Sc content had the strongest explanatory power to Hg content, and anthropogenic factors had the strongest explanatory power to Pb content. Interaction detection revealed that the q values of the strongest interaction explanatory power for As, Cd, Cr, Cu, Hg, Ni, Pb, and Zn were 2.81, 4.30, 1.26, 2.47, 2.33, 1.59, 6.37, and 5.08 times higher than their strongest factor detection explanatory power, respectively. The interaction between anthropogenic factors and other factors has an important influence on the spatial differentiation of heavy metals in the study area. Risk detection showed that the average contents of As, Cd, Cr, Cu, Hg, Ni, Pb and Zn were the highest in the subregions of MgO, TS, Sc, X6, X13, MgO, TN and X4, respectively. Comprehensive study shows that the spatial differentiation of As, Cd, Cr, Cu, Ni and Zn is mainly affected by natural factors, but there are also some anthropogenic factors, the spatial differentiation of Hg is affected by both natural factors and atmospheric deposition, and the spatial distribution characteristics of Pb are mainly affected by anthropogenic factors.


Geoaccumulation index (I geo )
The geoaccumulation index (I geo ) method can be used to compare the concentration of different heavy metals in soil and their pollution degree 19 .
where I geo is the soil accumulation index of heavy metal i; Ci is the measured value of soil heavy metal i; Bi is the reference value, and the soil background value of Qinghai-Tibet Plateau is selected; k is the correction coefficient, generally 1.5.The pollution degree of I geo can be divided into seven grades: I geo < 0, 0 ≤ I geo < 1, 1 ≤ I geo < 2, 2 ≤ I geo < 3, 3 ≤ I geo < 4, 4 ≤ I geo < 5 and I geo ≤ 5 correspond to unpolluted, mild polluted, moderate polluted, moderate-heavy polluted, heavy polluted, heavy-extreme polluted and extremely heavy polluted, respectively.

Enrichment factor (EF)
The enrichment factor (EF) is a useful index to distinguish between natural and anthropogenic sources of heavy metals.EF can be calculated based on the following functions 40 : (1) where [M i /M Sc ] S is the concentration ratio of the heavy metal i to Sc in samples, while [M i /M Sc ] B is the ratio of background values.Sc is a trace element, and has no significant anthropogenic sources, so Sc is chosen as the reference element 40 .Generally, according EF value the soils can be classified as deficiencyto minimal enrichment (< 1), mild enrichment (1-2), moderate enrichment (2-5), significant enrichment (5-20), very high enrichment (20-40), or extremely high enrichment (≥ 40)

Pollution index (PI) and synthetic pollution index (SPI)
In order to assess the level of HMs pollution in the soil, a single factor PI and SPI were calculated: where PI is the pollution index of element i and SPI is the synthetical score of each heavy metals to the composite pollution.S i is the evaluation standard of the i element, and the national control thresholds were chosen as the standard (Table 1).There are five pollution categories based on PI and SPI values: < 0.7, 0.7-1, 1-2, 2-3, ≥ 3, representing safety, alert, low pollution, moderate pollution, and severe pollution, respectively 41 .

Geographical detector
Geographic detector measure the contribution of independent variables to dependent variables by calculating the ratio of the sum of the variances of the respective variables after classification to the sum of the variances of the dependent variable, including factor detectors, interaction detectors, risk detectors, and ecological detectors 25 .
Factor detector: used to detect the spatial differentiation of dependent variables and the ability of their respective variables to explain the influence of dependent variables, measured by the value of q: where h = 1,…, L is the classification number of the independent variable X, N h and N are the classification h and the number of units in the whole region, σ 2 h and σ 2 the variance of the dependent variable Y in the classification h and the region, respectively.SSW and SST represent the sum of the variances of all categories of the independent variable X and the total variance in the region, respectively.The range of q is [0,1].The larger the value of q is, the greater the influence of the independent variable X on the dependent variable Y is.
Interaction detector by identifying the q value of the interaction between two different independent variables, the influence of the interaction between independent variables on the dependent variable is judged on the basis of: when q(X 1 ∩ X 2 ) < min(q(X 1 ), q(X 2 )), the interaction decreases nonlinearly; when min(q(X 1 ), q ( X 2 )) < q(X 1 ∩ X 2 ) < max(q(X 1 ), q(X 2 )), it is a single factor nonlinear weakening; when q(X 1 ∩ X 2 ) > max(q(X 1 ), q(X 2 )) is a double factor enhancement; when q(X 1 ∩ X 2 ) = q(X 1 ) + q(X 2 ), it is an independent interaction; when q(X 1 ∩ X 2 ) > q(X 1 ) + q(X 2 ) is nonlinear enhancement.
Risk detector it is mainly used to detect whether the influence factors are at risk to soil heavy metals, and t statistics are used to test it.
where Y h represents the mean value of attributes in sub-region h, in this study, the content of heavy metal ele- ments; Var represents variance; n h is the number of samples in sub-region h; the statistic t approximately obeys Student's t distribution, and the higher the t value, the greater the influence of the influence factor on the spatial differentiation of soil heavy metals.
Ecological detector it is used to compare whether there is a significant difference between the two factors on the spatial distribution of soil heavy metals, which is measured by F statistics.
where N X1 and N X2 represent the sample size of two independent variables X 1 and X 2 respectively; SSW X1 and SSW X2 represent the sum of intra-layer variances formed by X 1 and X 2 , respectively; and L 1 and L 2 represent the number of variables X 1 and X 2 , respectively.Where zero assumes H0: SSW X1 = SSW X2 .If H0 is rejected at the www.nature.com/scientificreports/significance level of α, it shows that there is a significant difference in the influence of two independent variables X 1 and X 2 on the spatial distribution of attribute dependent variable Y.

Factor index selection and data processing
Referring to the selection methods of other scholars' factor indicators, combined with the actual situation of the study area.Select soil properties (SiO 2 , Al 2 O 3 , CaO, TFe 2 O 3 , K 2 O, MgO, Na 2 O, total carbon (TC), organic carbon (C org ), total nitrogen (TN), total phosphorus (TP), total sulfur (TS), Sc and pH), normalized vegetation cover index (NDVI), topographic factors (elevation (X 1 ), slope (X 2 ), aspect (X 3 )), soil parent materials (X 4 ), soil types (X 5 ), soil erosion degree (X 6 ), distance factor (distance from railway (X 7 ), distance from national highway G109 (X 8 ), distance from county road (X 9 ), distance from pastoral point (X 11 ), distance from rural area (X 12 ), distance from lake (X 13 ), distance from river (X 14 )) 28 factors.Elevation data (GDEMDEM30m) comes from geospatial data cloud (http:// www.gsclo ud.cn).Because when using geographic detector to analyze the influencing factors, the dependent variable must be a numerical variable, the independent variable must be a type variable, and if the independent variable is a numerical variable, it needs to be discretized into type variables.In this study, the natural breakpoint method is used to classify the influencing factors, and the classification results are shown in Table 1.Descriptive statistical analysis and correlation analysis of the data are carried out by SPSS26.0,sampling map and spatial distribution map are drawn by ArcGIS10.8,mapping is completed by Origin2019, and geographic detector is completed by GeoDetector software (http:// www.geode tector.org/).

Basic properties of topsoil in the study area
The contents and physicochemical properties of heavy metals in topsoil in the study area are shown in Table 2.
The soils of all sampling sites are alkaline (pH > 7.5), the range of soil pH is 8.02-10.3, the average value is 8.67, higher than the background value of soil pH in Tibet and the geochemical baseline values of soil in Lhasa 42,43 42 , and the contents of heavy metals except Cu were higher than the geochemical baseline values of soil in Lhasa 43 .Many studies have pointed out that the coefficient of variation is proportional to the degree of interference from external factors such as human activities 4 .The high coefficient of variation of As, Cd, Pb and Zn in the study area indicates that there are great differences in their contents in different sampling sites, indicating that they may be affected by some external interference factors.Considering that atmospheric circulation is one of the most common ways for heavy metals to enter the terrestrial ecosystem of Tibet, the increase in the concentration of As, Cd, Pb and Zn in the study area may be attributed to the long-distance transport of heavy metals in the surrounding area 44 .The average content of As in the soil was higher than the soil pollution risk screening value (GB15618-2018), while the average contents of Cd, Cr, Cu, Hg, Ni, Pb and Zn were significantly lower than the soil pollution risk screening value.

Spatial distribution characteristics of soil heavy metals
Figure 2 shows the spatial distribution of 8 heavy metals in the soil of the study area.It can be seen that the high value areas of As are distributed in the southern and central regions, the high value areas of Cd are distributed in the west and south regions, the high value areas of Cr are distributed in the northwest and southwest regions, the high value areas of Cu are mainly distributed in a few regions in the west, the high value areas of Hg are mainly distributed in the southwest, the high value areas of Ni are concentrated in the northwest, central and southern regions, and the high value areas of Pb and Zn are mainly concentrated in the southern region.

Evaluation of soil heavy metals pollution
Based on I geo (Fig. 3a), the content of Cr in all samples was unpolluted; 98.44% of the samples were unpolluted with Cu and Ni, and 1.56% of the samples were mild to moderate polluted; for As, 67.97% of the samples was unpolluted, but 22.66%, 7.03% and 2.34% of the samples were mild, moderate and moderate-heavy polluted, respectively; for Cd, only 4.69% of the samples were unpolluted, but 43.75%, 43.75%, 5.47%, 1.56% and 0.78% were mild, moderate, moderate-heavy polluted, heavy polluted, respectively; for Hg, 96.09% of the samples were unpolluted, and 3.91% of the samples were mild polluted; for Pb, 58.59% of the samples were unpolluted, 34.38% and 6.25% of the samples were mild and moderate polluted; the Zn content in 84.38% of the samples was unpolluted, but 13.28%, 1.56% and 0.78% of the samples were mild, moderate and moderate-heavy polluted, respectively.Based on EF (Fig. 3b), Cr, Cu, Hg, and Ni of the samples exhibit similar enrichment phenomena, with 18.75%, 38.28%, 24.22%, and 25.00% had minimal enrichment, 80.47%, 60.94%, 69.53%, and 73.44% had mild enrichment, and 0.78%, 0.78%, 6.25%, and 1.56% had moderate enrichment, respectively; for As and Zn in the sample, 9.38% and 10.94% had minimal enrichment, 48.44% and 74.22% had mild enrichment, 35.94% and 14.06% a had moderate enrichment, and 6.25% and 0.78% had significantly enrichment, respectively; for Pb, 17.19%, 32.03%, 46.88%, 3.13% and 0.78% of samples had minimal enrichment, mild enrichment, moderate enrichment, significant enrichment and very high enrichment, respectively; all the samples had different degrees of Cd enrichment, and 7.03%, 55.47%, 36.72% and 0.78% of the samples had mild enrichment, moderate enrichment, significant enrichment and very high enrichment, respectively.www.nature.com/scientificreports/Based on PI (Fig. 3c), 100% of the samples tested for Cr, Hg and Ni content are safety; for Cu, 99.22% samples are safety, 0.78% are on alert; for Cd and Pb, 86.72% and 98.44% are safety, 7.81% and 0.79% are alert, and 0.78% are severe pollution; for Zn, the samples in safety, alert and low pollution accounted for 96.88%, 2.34% and 0.78% respectively; for As, the samples in safety, alert, low pollution, moderate pollution and severe pollution accounted for 14.06%, 35.94%, 39.84%, 6.25% and 3.91%, respectively.From the SPI, the samples with safety, alert, low pollution, moderate pollution and severe pollution are 42.19%,28.91%, 21.88%, 3.91% and 3.13%, respectively.
Generally, the I geo and EF of Cd, As, Pb and Zn in the study area were significantly higher than Hg, Cr, Ni and Cu.The samples with moderate and above pollution (I geo ≥ 2) of Cd, As, Pb and Zn accounted for 49.22%, 9.38%, 6.25% and 2.34%, respectively, and the samples with moderate and above enrichment (EF ≥ 2) of Cd, As, Pb and Zn accounted for 92.97%, 42.19%, 50.78% and 14.84%, respectively.On the one hand, it is related to the release of heavy metals in the diagenetic process of the study area, and it also means that there may be some external sources of heavy metals in the soil of the study area.SPI results show that 28.92% of the samples in the study area are in low pollution and above, and the environmental state varies from low pollution to serious pollution.

Relativity analysis
The results of correlation analysis are shown in Fig. 4. The results showed that there was a significant correlation among most heavy metals, but interestingly, there was no significant correlation among As-Cu, As-Hg, As-Pb, Pb-Cr, Pb-Cu, Pb-Hg and Pb-Ni.Among the influencing factors of soil properties (SiO 2 , Al 2 O 3 , CaO, TFe 2 O 3 , K 2 O,MgO, Na 2 O, TC, C org , TN, TP, TS, Sc and pH), Cr, Cr, Ni and Zn were significantly correlated with 8-11 of them, and As and Hg were significantly correlated with 6 of them, among which As showed a weak correlation, ranging from − 0.26 to 0.27, Cd and Pb only had significant correlation with 4 and 3 of them, and except Cd-TS, the other correlations were weak, especially Pb had weak correlation with C org , TN and TP, which were easy to transfer and transform in soil.There was no significant correlation between NDVI and 8 heavy metals.Among the topographic factors (X 1 , X 2 and X 3 ), only Cr, Cu, Hg, Ni and Zn showed significant positive correlation with X 1 and Ni-X 2 .In soil parent material (X 4 ), soil type (X 5 ) and soil erosion (X 6 ), only Ni and Zn showed weak correlation with X 4 , Cd-X 5 and Cd-X 6 .Among the distance factors (X 7 , X 8 , X 9 , X 11 , X 12 , X 13 and X 14 ), X 7 , X 8 and X 9 showed significant positive correlation with Cr, Cu and Hg, but significantly negative correlation with Pb.X 14 showed no significant correlation with heavy metals.X 11 showed significant positive correlation with Cd, Cr, Cu, Hg and Ni, but significant negative correlation with Pb.X 12 showed a significant negative correlation with Pb and Zn, but a significant positive correlation with Cr, while X 13 only had a significant positive correlation with Cr and   Pearson correlation coefficient of soil heavy metal elements and impact factors.Elevation (X 1 ), slope (X 2 ), aspect (X 3 ), soil parent materials (X 4 ), soil types (X 5 ), soil erosion degree (X 6 ), distance from railway (X 7 ), distance from national highway G109 (X 8 ), distance from county road (X 9 ), distance from pastoral point (X 11 ), distance from rural area (X 12 ), distance from lake (X 13 ), distance from river (X 14 ).www.nature.com/scientificreports/Hg.Generally, As, Cd, Cr, Cu, Hg, Ni and Zn in 8 kinds of heavy metals are greatly affected by natural factors, but they are affected by some external sources, which is consistent with the previous research results 30,44 , but the heavy metal Pb is quite different, and its spatial variation may be caused by external sources.

Factor detection
The explanatory power q value of 28 factors to 8 heavy metals detected by factor detector is shown in Fig. 5.There were significant differences in the explanatory power of different factors to 8 kinds of heavy metals.The main influencing factor of As is MgO, the q value is 0.295, the secondary influencing factor is SiO 2 (0.170) and the third influencing factors are TS, TC, Al 2 O 3 , CaO, Sc, X 13 , and TN, with q values ranging from 0.134 to 0.110.The primary influencing factor of Cd is TS (0.204), followed by X 5 , X 14 , X 13 , X 11 , K 2 O, X 6 , and TFe 2 O 3 , with q values ranging from 0.137 to 0.122, and the third influencing factors are X 1 (0.The first influencing factors of Pb are X 12 (0.150) and X 8 (0.135), the second influencing factors are X 7 and TN, with q values of 0.123, and the third influencing factors are X 14 , X 9 , NDVI, TP, C org , K 2 O, X 11 , and TS, with q values ranging from 0.110 to 0.082.The primary influencing factor of Zn is Sc (0.171), followed by X 1 , TFe 2 O 3 , X 14 , and X 12 , with q values ranging from 0.132 to 0.117, the third influencing factors are TS, Al 2 O 3 , MgO, TN, NDVI, X 7 , TC, X 4 , X 8 , and X 11 , with q values ranging from 0.112 to 0.081.The order of influence degree of different influence factors on different heavy metals is different, which reveals the heterogeneity of different heavy metal change mechanisms.From the main influencing factors of heavy metals, except Pb, Zn and Hg, the other five heavy metals were mainly affected by soil properties, indicating that the spatial distribution characteristics of soil As, Cd, Cr, Cu, Ni and Zn in the study area were mainly affected by natural factors.It is interesting that the spatial distribution characteristics of Hg are most closely related to the distance from the lake (X 13 ) and soil Sc content, as well as to altitude (X 1 ).Correlation analysis shows a highly significant positive correlation (p < 0.01) between Hg-X 13 and Hg-X 1 , indicating that soil Hg in the study area may be closely related to the long-distance transportation and sedimentation of Hg in the atmospheric circulation while being affected by the soil parent material.This is similar to many previous research conclusions [44][45][46][47] .The main factors affecting the spatial heterogeneity of soil Pb in the study area are the distance from the countryside (X 12 ) and the distance from G109 (X 8 ).The distance from the railway (X 7 ) and the county road (X 9 ) are also important factors affecting the spatial heterogeneity of soil Pb, which further shows that the spatial distribution of soil Pb in this area is mainly affected by human factors.Liu 44 have also studied the content of heavy metals in typical grassland soils in Tibet and believe that Pb in topsoil may come from atmospheric deposition caused by traffic emissions and industrial point sources.Zhang 40 pointed out that the concentration of heavy metal Pb in Tibetan soil decreased with the increase of distance from the road.Although the primary influencing factor of Zn is Sc, its q value of 0.171 is only 1.46 times of the q values of X 12 (0.117), and only 1. o: NDVI p: X 1 q: X 2 r: X 3 s: X 4 t: X 5 u: X 6 v: X 7 w: X 8 x: X 9 z: X 11 A: X 12 B: X 13 C: X 14 www.nature.com/scientificreports/human factors X 7 (0.086), X 8 (0.085) and X 11 (0.081).In addition, the correlation coefficient of Pb-Zn is as high as 0.832 (p < 0.01), so it can be inferred that Zn in the study area is affected by natural factors as well as human factors to a large extent.From the point of view of the primary, secondary and third influencing factors with the greatest explanatory power, the spatial differentiation of As, Cd, Cr, Cu, Ni and Zn in soil heavy metals in the study area is mainly caused by natural factors, but also affected by certain anthropogenic factors, which is basically consistent with the results of Pearson correlation analysis (Fig. 4).The spatial differentiation of Hg is affected by both natural factors and atmospheric deposition.The spatial distribution of Pb is mainly affected by anthropogenic factors.

Interaction detection
The composition and structure of soil are complex, and the spatial distribution and pollution of heavy metals are usually formed by many factors, so it is impossible for a single factor to affect the distribution and change of heavy metals 4 .Therefore, using the interaction detector to analyze the interaction degree of various factors on the spatial distribution of heavy metals is helpful to accurately judge the deep driving mechanism that affects the spatial distribution of heavy metals 48 .
The factor detection results show that the degree of explanation of the interaction of any two factors on the spatial differentiation of eight heavy metals is greater than that of a single factor, and most of them are nonlinear enhancement and a few are double factor enhancement, there is no weakening or independent type of action.For As is concerned (Fig. 6(1)), the strongest interactions are CaO ∩ X 3 , Al 2 O 3 ∩ X 11 and SiO 2 ∩ X 3 , with q values of 0.830, 0.827 and 0.792, respectively, which are 2.68-2.81times of the maximum factor detection q value of As (0.295).In addition, it can be seen that the distance from the herdsmen point (X 11) as a human factor also affects the distribution of soil As in this area.It may be caused by the long-term burning of yak manure and garbage incineration by local herdsmen 46,49 .For Cd (Fig. 6(2)), the interaction between TN ∩ X 14 (0.881) and X 3 ∩ X 14 (0.875) is the strongest, approximately 4.3 times its maximum factor detection q value (0.204).In addition, CaO ∩ TN, TC ∩ TS, TC ∩ X 8 , TC ∩ X 12 , C org ∩ X 7 , C org ∩ X 8 , C org ∩ X 9 , TN ∩ X 9 , TN ∩ X 12 , TP ∩ X 1 , TS ∩ X 1 , TS ∩ X 9 , X 3 ∩ X 11 , X 3 ∩ X 12 , X 9 ∩ X 13 and X 13 ∩ X 14 are all above 0.8, further indicates that although factors such as X 7 , X 8 and X 9 are not the main factors affecting the distribution of Cd in local soil, there is also a certain degree of influence.In addition, the strong migration, transformation and mobility of C, N, P, S with other influencing factors have a strong interaction on the Cd of the study area, which makes the migration mobility of Cd in this area is greater with the wetting of rain water, which is one of the possible reasons for the high Cd content of the Tuotuo river in the lower reaches of the region 39 .For Cr (Fig. 6(3)), the strongest interactions are TFe 2 O 3 ∩ X 1 (0.906), K 2 O ∩ Sc (0.898) and Sc ∩ X 9 (0.897), compared with their maximum factor detection q value (0.712), the explanatory power q value is increased by about 126%.What is interesting is that most of the interactions between Sc and TFe 2 O 3 and other influencing factors are above 0.8.Except for Sc and TFe 2 O 3 , the interaction between other factors on Cd was less than 0.8.For Cu (Fig. 6(4)), the largest q values of interaction are C org ∩ Sc (0.978), TN ∩ Sc (0.976) and TFe 2 O 3 ∩ X 3 (0.976).Compared with their maximum factor detection q value (0.395), the explanatory power q value is increased by about 247%.In addition, the q value of X 7 , X 8 and X 9 interaction with other influencing factors is also more than 0.976.It can be seen that anthropogenic factors have Figure 6.Interaction of different influence factors on soil heavy metals.Elevation (X 1 ), slope (X 2 ), aspect (X 3 ), soil parent materials (X 4 ), soil types (X 5 ), soil erosion degree (X 6 ), distance from railway (X 7 ), distance from national highway G109 (X 8 ), distance from county road (X 9 ), distance from pastoral point (X 11 ), distance from rural area (X 12 ), distance from lake (X 13 ), distance from river (X 14 ).a certain influence on the spatial differentiation of soil Cd in the study area.For Hg (Fig. 6(5)), the interaction of TFe 2 O 3 ∩ X 1 , Al 2 O 3 ∩ X 1 and SiO 2 ∩ X 13 are the strongest, with q values of 0.840, 0.836 and 0.795, respectively, which are 2.20-2.33 times of their maximum factor detection q values (0.361).For Ni (Fig. 6(6)), the interaction between MgO ∩ TP (0.877), K 2 O ∩ Sc (0.868) and TFe 2 O 3 ∩ Na 2 O (0.863) are the strongest, which is about 159% higher than its maximum factor detection q value (0.544).For Pb (Fig. 6(7)), the interaction between TC ∩ X 8 (0.962), TC ∩ X 12 (0.956) and X 2 ∩ X 8 (0.956) are the strongest, which is about 637% higher than its maximum factor detection q value (0.150).For Zn (Fig. 6(8)), the interaction between X 2 ∩ X 12 (0.875), SiO2 ∩ NDVI (0.868) and Sc ∩ X 9 (0.867) are the strongest, which is about 508% higher than its maximum factor detection q value (0.171).
After careful observation, it was found that any two of the 28 influencing factors showed similar changes in the interaction between Pb and Zn, and the high or low values of q detected by the two heavy metals appeared in the interaction of the same factor pairs, which further confirmed the conclusion of factor detection that Zn was affected by natural factors as well as anthropogenic factors to a great extent.Generally, although the explanatory power q values of human factors in factor detection are relatively small, through the interactive detection results, it can be found that the interaction of these anthropogenic factors and other factors has an important impact on the spatial differentiation of heavy metals in this region.The interaction of various factors can better explain the spatial heterogeneity of heavy metals and provide interesting information.
According to the 28 factors, the average content of As was the highest in the sub-region where the MgO < 2.46% (L10), the average content of 115 mg/kg; Cd was the highest in the L10 sub-region of TS (> 504 mg/ kg), at 0.79 mg/kg; Cr was the highest in the L10 sub-region (> 12.6 mg/kg) of Sc, which was 90.4 mg/kg.The average content of Cu was the highest in the L1 sub-region (no erosion) of the influence factor X 6 , which was 36.8 mg/kg.The average content of Hg in the L10 sub-region of influence factor X 13 (> 7103 m) is the highest, which is 0.0477 mg/kg.The average content 40.8 mg/kg of Ni is the highest in the sub-region where the influence factor MgO content is more than 2.46% (L10).The average content 110.0 mg/kg of Pb is the highest in the L9 sub-region (1394-1739 mg/kg) of the influencing factor TN. The average content 196.0 mg/kg of Zn in the L10 sub-region (slope and alluvial parent materia) of influence factor X 4 was the highest.In addition, the analysis shows that the results of risk detection are consistent with the results of factor detection, that is, factor detection has great explanatory power on heavy metals, and there are significant differences in the content of heavy metals among their subregions.

Ecological detection
Ecological detection focuses on comparing whether there is a significant difference between one influence factor and another influence factor on the spatial distribution of soil heavy metals 4 , if significant, it will be recorded as Y, otherwise it will be recorded as N.
The ecological detection results of soil heavy metals in the study area showed that there were significant differences in the effects of MgO with TFe 2 O 3 and K 2 O on As (Fig. 8(1)), but there were no significant differences among other factors.There are significant differences in the effects of TFe 2 O 3 with SiO 2 , Al 2 O 3 and CaO, MgO with SiO 2 , Al 2 O 3 , CaO and K 2 O, Sc with SiO 2 , Al 2 O 3 , CaO, TFe 2 O 3 , K 2 O, MgO, Na 2 O, TC, C org , TN, TP and TS, and X 6 with X 7 and X 8 on Cr (Fig. 8(3)), but there are no significant differences among other factors.There were significant differences in the effects of TFe 2 O 3 with SiO2 and CaO and Sc with SiO 2 , CaO, K 2 O, MgO, Na 2 O, TC, C org , TN, TP and TS on Cu (Fig. 8(4)), but there were no significant differences among other factors.The effects of Sc with SiO 2 , K 2 O, MgO, Na 2 O, TC, C org , TN, TP and TS, X 1 with TC and C org , and X 13 with SiO 2 , CaO, TFe 2 O 3 , K 2 O, MgO, TC, C org , TN, TP, TS, Sc, pH, NDVI, X 2 , X 3 , X 4 , X 5 , X 6 , X 7 , X 8 , X 9 , X 11 and X 12 on Hg were significantly different (Fig. 8(5)), but there were no significant differences among other factors.There were significant differences in the effects of Al

Figure 3 .
Figure 3. Scatter diagram for geoaccumulation index (I geo ), enrichment factor (EF), and pollution index for As, Cd, Cr, Cu, Hg, Ni, Pb and Zn in this study.
2 b: Al 2 O 3 c: CaO d: TFe 2 O 3 e: K 2 O f: MgO g: Na 2 O h: TC i: C org j: TN k: TP l: TS m: Sc n: pH

Figure 5 .
Figure 5. Effects of different factors on the explanatory power of eight heavy metals in soils with q value.

Figure 7 .
Figure 7. Risk detection of heavy metals content.

Table 2 .
Descriptive statistical results of soil composition.